• pandas Dataframe - Basic Operativity
  • 1 File I/O and DataFrame Generation
    • 1.1 Create DataFrames with read_csv
    • 1.2 Create DataFrames from Python Dictionaries
    • 1.3 Create DataFrames from Items
    • 1.4 Create DataFrames fron Numpy Arrays
    • 1.5 DataFrames can be converted in Numpy Arrays
    • 1.6 DataFrames, Series and Panels
  • 2 Automatic Data Alignment
  • 3 Indexing
    • 3.1 Label-Based Indexing
    • 3.2 Position-Based Indexing
    • 3.3 Advanced Indexing - .ix
  • 4 DataFrame Basic Operations
    • 4.1 Reindex/Reorder rows and columns
    • 4.2 Calculate new columns
    • 4.3 Deleting rows and columns
    • 4.4 Inserting colums in a specific position
    • 4.5 Check if a value or a list of given values are contained in a specific column
    • 4.6 Rename columns
    • 4.7 Iterate efficiently through rows
  • 5 Duplicated Data
    • 5.1 Find duplicated data in columns
    • 5.2 Remove Duplicates
  • 6 Working with Large Arrays
    • 6.1 Control the DataFrame memory occupation
    • 6.2 Explore large arrays
  • 7 Column pct_change and shift
  • 8 Reindex
  • 9 More on Indexing: Multi Index
  • 10 Package Options
• pandas I/O tools and examples
  • 1 Matlab Variables
    • 1.1 Import a Matlab variable from file
  • 2 Importing a compressed CSV
  • 3 Importing and visualizing geographical data
  • 4 Importing JSON files
  • 5 Importing HTML
  • 6 Importing Excel
  • 7 Working with SQL and databases
    • 7.1 Write SQL
    • 7.2 Import SQL
  • 8 Working with HDF5
    • 8.1 Storer format
    • 8.2 Table format
    • 8.3 Querying a Table
• Pandas Time series
  • 1 Timestamps and DatetimeIndex
  • 2 DateOffsets objects
  • 3 Indexing with a DateTime index
  • 4 Frequency conversion
  • 5 Filling gaps
• Statistical tools
  • 1 Percent change
  • 2 Covariance
  • 3 Correlation
  • 4 Rolling moments and Binary rolling moments
  • 5 A pratical example: Return indexes and cumulative returns
• Merge and pivot
  • 1 Concat
  • 2 Append
  • 3 Join
  • 4 Merge
  • 5 Pivoting
  • 6 Stack and Unstack
• Split apply and combine
  • 1 Groupby
  • 2 Aggregate
  • 3 Apply
  • 4 A pratical example: Normalize by year
  • 5 A practical example: Group and standardize by dimension
• Sources of Open Data
  • 1 Yahoo! Finance
    • 1.1 Plotting timeseries with bokeh:
    • 1.2 Plotting candlesticks with bokeh:
    • 1.3 Plotting data ranges with bokeh:
    • 1.4 Plotting multiple plots with matplotlib:
  • 2 Google Finance
  • 3 Federal Reserve Economic Data
  • 4 World Bank
• Baby Names
  • 1 Load and prepare the data
  • 2 Pivoting
  • 3 Splitting
  • 4 Using 'groupby'